Which solution will meet these requirements?
Establish a connection between the frontend application and the database to make queries faster by bypassing the API.
Configure provisioned concurrency for the Lambda function that handles the requests.
Cache the results of the queries in Amazon S3 for faster retrieval of similar datasets.
Increase the size of the database to increase the number of connections Lambda can establish at one time.
Explanations:
Establishing a direct connection between the frontend application and the database bypasses the API Gateway, which can lead to security issues, loss of API management features, and increased complexity in handling database connections directly from the frontend. This approach may also not necessarily improve response latency as it introduces new potential points of failure and does not leverage existing backend processing optimally.
Configuring provisioned concurrency for the Lambda function ensures that a set number of instances are always ready to handle incoming requests. This reduces cold start latency significantly, which is crucial for maintaining low response times, especially during peak traffic periods. It allows the Lambda function to scale quickly while minimizing latency for end-users.
Caching the results of queries in Amazon S3 may not provide low latency, as retrieving data from S3 is generally slower than accessing it directly from a database. Additionally, this approach could introduce complexity in managing cache validity and ensuring users receive the most up-to-date data, which may not be suitable for real-time application requirements.
Increasing the size of the database may allow for more connections but does not directly address response latency. The database performance may still be constrained by query efficiency and indexing. Additionally, simply increasing resources does not guarantee improved response times for the Lambda function or API Gateway integration, as latency can also be affected by other factors in the request handling process.